resources/segunda-entrega/TestingModel.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluación de los modelos entrenados"
      ],
      "metadata": {
        "id": "-Mu3lFYIPHHG"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Cs2JQ4xiajGT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9aee81da-c6e2-49ae-a9d3-470ad533f6d0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 5.8 MB 4.8 MB/s \n",
            "\u001b[K     |████████████████████████████████| 182 kB 85.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 7.6 MB 65.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 72 kB 675 kB/s \n",
            "\u001b[K     |████████████████████████████████| 132 kB 9.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 451 kB 66.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 212 kB 88.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 127 kB 89.4 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install transformers -q\n",
        "!pip install evaluate -q"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "etMyd9LbGU0b"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from transformers import BertTokenizer, BertForQuestionAnswering\n",
        "from transformers import pipeline\n",
        "\n",
        "from pathlib import Path\n",
        "from urllib.request import urlopen\n",
        "import json"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Tomado de https://huggingface.co/spaces/evaluate-metric/squad\n",
        "from evaluate import load\n",
        "squad_metric = load(\"squad\")"
      ],
      "metadata": {
        "id": "AANG_e1mPung",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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          ]
        },
        "outputId": "79cffa8b-7f2f-4125-8666-0b7647140d97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.53k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "348b0a13ee7b4b5ea639af71912eeb44"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading extra modules:   0%|          | 0.00/3.32k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8925b3eac8cb4020a26332d7e8996005"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Carga de datos"
      ],
      "metadata": {
        "id": "5S8a-K1IPPoJ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gndZwUy5Fv2_"
      },
      "outputs": [],
      "source": [
        "dataset={\n",
        "'Arabic':'xquad.ar.json','German':'xquad.de.json','Greek':'xquad.el.json','English':'xquad.en.json',\n",
        "'Spanish':'xquad.es.json','Hindi':'xquad.hi.json','Russian':'xquad.ru.json','Thai':'xquad.th.json',\n",
        "'Turkish':'xquad.tr.json','Vietnamese':'xquad.vi.json','Chinese':'xquad.zh.json','Romanian':'xquad.ro.json'\n",
        "}\n",
        "\n",
        "base_url='https://raw.githubusercontent.com/deepmind/xquad/master/{}'\n",
        "\n",
        "def read_crossxsquad(language, is_url=True):\n",
        "  if is_url:\n",
        "    path=base_url.format(dataset[language])\n",
        "    response = urlopen(path)\n",
        "    squad_dict = json.loads(response.read())\n",
        "  else:\n",
        "    path = Path(path)\n",
        "    squad_dict = json.load(path)\n",
        "  \n",
        "  es_squad_dict = json.loads(urlopen('https://raw.githubusercontent.com/deepmind/xquad/master/xquad.es.json').read())\n",
        "  data=[]\n",
        "  \n",
        "  for group, group_es in zip(squad_dict['data'], es_squad_dict['data']):\n",
        "    for passage, passage_es in zip(group['paragraphs'], group_es['paragraphs']):\n",
        "      context = passage['context']\n",
        "      context_es = passage_es['context']\n",
        "      for qa, qa_es in zip(passage['qas'], passage_es['qas']):\n",
        "        question = qa['question']\n",
        "        question_es = qa_es['question']\n",
        "        for answer, answer_es in zip(qa['answers'], qa_es['answers']):\n",
        "          data.append((context, question_es, answer))\n",
        "    \n",
        "  return data\n",
        "\n",
        "def split_squad(data, train_size=10, only_testing=True):\n",
        "  data_train, data_test = train_test_split(data,train_size=train_size, random_state=13)\n",
        "  train_contexts = [p[0] for p in data_train]\n",
        "  train_questions = [p[1] for p in data_train]\n",
        "  train_answers = [p[2] for p in data_train]\n",
        "  test_contexts = [p[0] for p in data_test]\n",
        "  test_questions = [p[1] for p in data_test]\n",
        "  test_answers = [p[2] for p in data_test]\n",
        "  return (train_contexts, train_questions, train_answers), (test_contexts, test_questions, test_answers)\n",
        "\n",
        "data_cross_XQuAD={}\n",
        "for language in dataset.keys(): \n",
        "  data_cross_XQuAD[language]={}\n",
        "  language_data = read_crossxsquad(language)\n",
        "  _, test_data = split_squad(language_data, train_size=10)\n",
        "  data_cross_XQuAD[language]['test_contexts'] = test_data[0]\n",
        "  data_cross_XQuAD[language]['test_questions'] = test_data[1]\n",
        "  data_cross_XQuAD[language]['test_answers'] = test_data[2]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Carga de los modelos"
      ],
      "metadata": {
        "id": "hcrYotP5P9WB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "models={\n",
        "    'BERT-XQuAD': 'mrm8488/bert-multi-cased-finetuned-xquadv1',\n",
        "    'BERT-CrossXQuAD-1Sample': 'LeoAngel/bert-finetuned-crossxquadv1_1sbl',\n",
        "    'BERT-CrossXQuAD-2Samples': 'LeoAngel/bert-finetuned-crossxquadv1_2sbl',\n",
        "    'BERT-CrossXQuAD-5Samples':'LeoAngel/bert-finetuned-crossxquadv1_5sbl',\n",
        "    'BERT-CrossXQuAD-10Samples':'LeoAngel/bert-finetuned-crossxquadv1_10sbl',\n",
        "    'BERT-CrossXQuAD-20Samples':'LeoAngel/bert-finetuned-crossxquadv1_20sbl',\n",
        "    'BERT-CrossXQuAD-25Samples':'LeoAngel/bert-finetuned-crossxquadv1_25sbl'\n",
        "    }"
      ],
      "metadata": {
        "id": "bRuRL8MmQBQF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluación sobre Cross-XQuAD (spanish)"
      ],
      "metadata": {
        "id": "JGJBTAMfD_KL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A continuación se evaluará cada uno de los modelos entrenados en el notebook CrossLanguageExperiment.ipynb. Para el dataset Cross-XQuAD (spanish) que consiste en triplas de *(Contexto, Pregunta, Respuesta)* donde *Pregunta* siempre está en español y, *Contexto* y *Pregunta* están en el mismo idioma (no necesariamente español)."
      ],
      "metadata": {
        "id": "KPQBZbNAcK9G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Al ejecutar este notebook, se debe ajustar el número de registros que se usarán por idioma para evaluar el modelo. Si no es muy demorado, se recomienda usar el máximo."
      ],
      "metadata": {
        "id": "3KDCwFWsdpO9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "max_samples_by_language = 50 #@param {type:\"slider\", min:0, max:5000, step:1}"
      ],
      "metadata": {
        "id": "P_0oTpKycj81"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_XQuAD(pipeline, data_to_evaluate):\n",
        "  predictions={}\n",
        "  answers={}\n",
        "\n",
        "  for language in dataset.keys():\n",
        "    predictions[language]=[]\n",
        "    answers[language]=[]\n",
        "    tupled_data=zip(data_to_evaluate[language]['test_contexts'],\n",
        "                    data_to_evaluate[language]['test_questions'],\n",
        "                    data_to_evaluate[language]['test_answers'])\n",
        "    for idx, (context, question, answer) in enumerate(tupled_data):\n",
        "      prediction = pipeline({'context': context,\n",
        "                             'question': question})\n",
        "      predictions[language].append({'prediction_text': prediction['answer'], 'id': str(idx)})\n",
        "      formatted_answer={'answers': {'answer_start': [answer['answer_start']], 'text': [answer['text']]},\n",
        "                        'id': str(idx)}\n",
        "      answers[language].append(formatted_answer)\n",
        "      if idx >= max_samples_by_language:\n",
        "        break\n",
        "  results={}\n",
        "  for language in dataset.keys():\n",
        "    results[language] = squad_metric.compute(predictions=predictions[language],\n",
        "                                            references=answers[language])\n",
        "  results=[]\n",
        "  for language in dataset.keys():\n",
        "    new_result=squad_metric.compute(predictions=predictions[language],\n",
        "                                    references=answers[language])\n",
        "    new_result['language']=language\n",
        "    results.append(new_result)\n",
        "  return pd.DataFrame.from_dict(results).set_index('language').sort_values(by='f1', ascending=False)"
      ],
      "metadata": {
        "id": "lRBoSCO1UZYr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### BERT junto con fine-tuning sobre XQuAD\n",
        "Este corresponde al modelo base: BERT entrenado con XQuAD. Este modelo no fue entrenado para el dataset Cross-XBERT-spanish."
      ],
      "metadata": {
        "id": "0zJioBZHVPlN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name='BERT-XQuAD'\n",
        "url_model=models[model_name]\n",
        "qa_pipeline = pipeline(\"question-answering\", model=url_model, tokenizer=url_model)\n",
        "\n",
        "print(model_name)\n",
        "print('_'*70+'\\n'+'Results over Cross-XQuAD', end='\\n'+'_'*70)\n",
        "evaluate_XQuAD(qa_pipeline, data_cross_XQuAD)"
      ],
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        {
          "output_type": "stream",
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          "text": [
            "BERT-XQuAD\n",
            "______________________________________________________________________\n",
            "Results over Cross-XQuAD\n",
            "______________________________________________________________________"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            exact_match         f1\n",
              "language                          \n",
              "Spanish       96.078431  98.996265\n",
              "English       94.117647  95.798319\n",
              "German        90.196078  90.686275\n",
              "Arabic        76.470588  82.945268\n",
              "Russian       74.509804  81.305633\n",
              "Romanian      64.705882  77.323605\n",
              "Vietnamese    70.588235  75.470588\n",
              "Chinese       68.627451  71.241830\n",
              "Greek         56.862745  68.499066\n",
              "Hindi         58.823529  67.846314\n",
              "Turkish       54.901961  66.042422\n",
              "Thai          47.058824  54.295051"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>exact_match</th>\n",
              "      <th>f1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>language</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Spanish</th>\n",
              "      <td>96.078431</td>\n",
              "      <td>98.996265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>English</th>\n",
              "      <td>94.117647</td>\n",
              "      <td>95.798319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>German</th>\n",
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              "      <td>90.686275</td>\n",
              "    </tr>\n",
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              "      <td>76.470588</td>\n",
              "      <td>82.945268</td>\n",
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              "      <td>81.305633</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Romanian</th>\n",
              "      <td>64.705882</td>\n",
              "      <td>77.323605</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vietnamese</th>\n",
              "      <td>70.588235</td>\n",
              "      <td>75.470588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "    </tr>\n",
              "    <tr>\n",
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              "      <td>56.862745</td>\n",
              "      <td>68.499066</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>Turkish</th>\n",
              "      <td>54.901961</td>\n",
              "      <td>66.042422</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thai</th>\n",
              "      <td>47.058824</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Los modelos subsecuentes fueron usando como base el anterior, y usando n datos de entrenamiento por idioma (n<=25)."
      ],
      "metadata": {
        "id": "womwFuDGeDfO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## BERT-CrossXQuAD usando solo un ejemplo de entrenamiento por idioma"
      ],
      "metadata": {
        "id": "8g3nF8s5iNeo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name='BERT-CrossXQuAD-1Sample'\n",
        "url_model=models[model_name]\n",
        "qa_pipeline = pipeline(\"question-answering\", model=url_model, tokenizer=url_model)\n",
        "\n",
        "print(model_name)\n",
        "print('_'*70+'\\n'+'Results over Cross-XQuAD', end='\\n'+'_'*70)\n",
        "evaluate_XQuAD(qa_pipeline, data_cross_XQuAD)"
      ],
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          "text": [
            "BERT-CrossXQuAD-1Sample\n",
            "______________________________________________________________________\n",
            "Results over Cross-XQuAD\n",
            "______________________________________________________________________"
          ]
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              "            exact_match         f1\n",
              "language                          \n",
              "Spanish       84.313725  87.058824\n",
              "English       80.392157  81.372549\n",
              "Russian       62.745098  66.375944\n",
              "German        52.941176  59.155354\n",
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              "Romanian      41.176471  49.774096\n",
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              "Greek         41.176471  48.193277\n",
              "Turkish       35.294118  45.644532\n",
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              "      <td>66.375944</td>\n",
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              "      <td>59.155354</td>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-16a5cb85-55f1-4a96-b39d-88a7da7d2aef');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### BERT-CrossXQuAD usando 2 ejemplos de entrenamiento por idioma"
      ],
      "metadata": {
        "id": "8CtUtRTVjCT_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name='BERT-CrossXQuAD-2Samples'\n",
        "url_model=models[model_name]\n",
        "qa_pipeline = pipeline(\"question-answering\", model=url_model, tokenizer=url_model)\n",
        "\n",
        "print(model_name)\n",
        "print('_'*70+'\\n'+'Results over Cross-XQuAD', end='\\n'+'_'*70)\n",
        "evaluate_XQuAD(qa_pipeline, data_cross_XQuAD)"
      ],
      "metadata": {
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          "output_type": "stream",
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          "text": [
            "BERT-CrossXQuAD-2Samples\n",
            "______________________________________________________________________\n",
            "Results over Cross-XQuAD\n",
            "______________________________________________________________________"
          ]
        },
        {
          "output_type": "execute_result",
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              "            exact_match         f1\n",
              "language                          \n",
              "Spanish       66.666667  75.355933\n",
              "Vietnamese    41.176471  46.469734\n",
              "German        39.215686  44.110791\n",
              "English       35.294118  42.119514\n",
              "Hindi         29.411765  37.071034\n",
              "Romanian      25.490196  35.620799\n",
              "Greek         27.450980  35.420168\n",
              "Russian       27.450980  34.625881\n",
              "Turkish       25.490196  34.097691\n",
              "Arabic        27.450980  31.770212\n",
              "Chinese       23.529412  27.450980\n",
              "Thai          21.568627  24.229692"
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              "      <td>66.666667</td>\n",
              "      <td>75.355933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vietnamese</th>\n",
              "      <td>41.176471</td>\n",
              "      <td>46.469734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>39.215686</td>\n",
              "      <td>44.110791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>English</th>\n",
              "      <td>35.294118</td>\n",
              "      <td>42.119514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hindi</th>\n",
              "      <td>29.411765</td>\n",
              "      <td>37.071034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Romanian</th>\n",
              "      <td>25.490196</td>\n",
              "      <td>35.620799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Greek</th>\n",
              "      <td>27.450980</td>\n",
              "      <td>35.420168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Russian</th>\n",
              "      <td>27.450980</td>\n",
              "      <td>34.625881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Turkish</th>\n",
              "      <td>25.490196</td>\n",
              "      <td>34.097691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arabic</th>\n",
              "      <td>27.450980</td>\n",
              "      <td>31.770212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chinese</th>\n",
              "      <td>23.529412</td>\n",
              "      <td>27.450980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thai</th>\n",
              "      <td>21.568627</td>\n",
              "      <td>24.229692</td>\n",
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              "          const element = document.querySelector('#df-75db86b0-b861-41e4-a773-05267af91454');\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### BERT-CrossXQuAD usando 5 ejemplos de entrenamiento por idioma"
      ],
      "metadata": {
        "id": "z1zURTTvjIhY"
      }
    },
    {
      "cell_type": "code",
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        "model_name='BERT-CrossXQuAD-5Samples'\n",
        "url_model=models[model_name]\n",
        "qa_pipeline = pipeline(\"question-answering\", model=url_model, tokenizer=url_model)\n",
        "\n",
        "print(model_name)\n",
        "print('_'*70+'\\n'+'Results over Cross-XQuAD', end='\\n'+'_'*70)\n",
        "evaluate_XQuAD(qa_pipeline, data_cross_XQuAD)"
      ],
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            "______________________________________________________________________\n",
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    {
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      "source": [
        "### BERT-CrossXQuAD usando 10 ejemplos de entrenamiento por idioma"
      ],
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      }
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        "url_model=models[model_name]\n",
        "qa_pipeline = pipeline(\"question-answering\", model=url_model, tokenizer=url_model)\n",
        "\n",
        "print(model_name)\n",
        "print('_'*70+'\\n'+'Results over Cross-XQuAD', end='\\n'+'_'*70)\n",
        "evaluate_XQuAD(qa_pipeline, data_cross_XQuAD)"
      ],
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            "______________________________________________________________________\n",
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              "  <tbody>\n",
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              "      <th>Spanish</th>\n",
              "      <td>58.823529</td>\n",
              "      <td>65.423647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>English</th>\n",
              "      <td>43.137255</td>\n",
              "      <td>49.936382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>German</th>\n",
              "      <td>37.254902</td>\n",
              "      <td>40.506117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hindi</th>\n",
              "      <td>35.294118</td>\n",
              "      <td>39.300046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vietnamese</th>\n",
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              "      <td>36.992317</td>\n",
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              "    <tr>\n",
              "      <th>Chinese</th>\n",
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              "      <td>23.529412</td>\n",
              "      <td>28.947200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>21.568627</td>\n",
              "      <td>27.561744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Greek</th>\n",
              "      <td>21.568627</td>\n",
              "      <td>27.550717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Russian</th>\n",
              "      <td>23.529412</td>\n",
              "      <td>24.888097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thai</th>\n",
              "      <td>19.607843</td>\n",
              "      <td>24.360411</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arabic</th>\n",
              "      <td>19.607843</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### BERT-CrossXQuAD usando 20 ejemplos de entrenamiento por idioma"
      ],
      "metadata": {
        "id": "pGeoAHENjV6R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name='BERT-CrossXQuAD-20Samples'\n",
        "url_model=models[model_name]\n",
        "qa_pipeline = pipeline(\"question-answering\", model=url_model, tokenizer=url_model)\n",
        "\n",
        "print(model_name)\n",
        "print('_'*70+'\\n'+'Results over Cross-XQuAD', end='\\n'+'_'*70)\n",
        "evaluate_XQuAD(qa_pipeline, data_cross_XQuAD)"
      ],
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BERT-CrossXQuAD-20Samples\n",
            "______________________________________________________________________\n",
            "Results over Cross-XQuAD\n",
            "______________________________________________________________________"
          ]
        },
        {
          "output_type": "execute_result",
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              "            exact_match         f1\n",
              "language                          \n",
              "Spanish       74.509804  79.315565\n",
              "English       70.588235  73.230134\n",
              "German        54.901961  60.826690\n",
              "Vietnamese    52.941176  58.655462\n",
              "Russian       47.058824  49.180036\n",
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              "Romanian      35.294118  46.415327\n",
              "Arabic        41.176471  44.242141\n",
              "Greek         39.215686  44.171874\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-aaa0ec95-c73d-4760-8026-849b8ae9ad6d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-aaa0ec95-c73d-4760-8026-849b8ae9ad6d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### BERT-CrossXQuAD usando 25 ejemplos de entrenamiento por idioma"
      ],
      "metadata": {
        "id": "yiuy4q10j5dO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name='BERT-CrossXQuAD-25Samples'\n",
        "url_model=models[model_name]\n",
        "qa_pipeline = pipeline(\"question-answering\", model=url_model, tokenizer=url_model)\n",
        "\n",
        "print(model_name)\n",
        "print('_'*70+'\\n'+'Results over Cross-XQuAD', end='\\n'+'_'*70)\n",
        "evaluate_XQuAD(qa_pipeline, data_cross_XQuAD)"
      ],
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BERT-CrossXQuAD-25Samples\n",
            "______________________________________________________________________\n",
            "Results over Cross-XQuAD\n",
            "______________________________________________________________________"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            exact_match         f1\n",
              "language                          \n",
              "English       68.627451  73.721886\n",
              "Spanish       64.705882  68.951277\n",
              "Vietnamese    45.098039  54.027596\n",
              "Chinese       43.137255  49.673203\n",
              "German        43.137255  49.193900\n",
              "Russian       47.058824  48.529412\n",
              "Turkish       39.215686  47.654623\n",
              "Hindi         37.254902  44.139194\n",
              "Romanian      31.372549  40.114899\n",
              "Arabic        31.372549  38.723499\n",
              "Greek         31.372549  38.050675\n",
              "Thai          17.647059  22.922502"
            ],
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>exact_match</th>\n",
              "      <th>f1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>language</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>English</th>\n",
              "      <td>68.627451</td>\n",
              "      <td>73.721886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Spanish</th>\n",
              "      <td>64.705882</td>\n",
              "      <td>68.951277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vietnamese</th>\n",
              "      <td>45.098039</td>\n",
              "      <td>54.027596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chinese</th>\n",
              "      <td>43.137255</td>\n",
              "      <td>49.673203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>German</th>\n",
              "      <td>43.137255</td>\n",
              "      <td>49.193900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Russian</th>\n",
              "      <td>47.058824</td>\n",
              "      <td>48.529412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Turkish</th>\n",
              "      <td>39.215686</td>\n",
              "      <td>47.654623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hindi</th>\n",
              "      <td>37.254902</td>\n",
              "      <td>44.139194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Romanian</th>\n",
              "      <td>31.372549</td>\n",
              "      <td>40.114899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arabic</th>\n",
              "      <td>31.372549</td>\n",
              "      <td>38.723499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Greek</th>\n",
              "      <td>31.372549</td>\n",
              "      <td>38.050675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thai</th>\n",
              "      <td>17.647059</td>\n",
              "      <td>22.922502</td>\n",
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              "  </tbody>\n",
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              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e396d7cf-d3e7-4328-809a-638c915f5904 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e396d7cf-d3e7-4328-809a-638c915f5904');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluación sobre XQuAD"
      ],
      "metadata": {
        "id": "tsuX6_sRk_sG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Con esta evaluación se pretender revisar qué tanta capacidad pierde o gana cada modelo de QA después de ser entrenado sobre Cross-XQuAD. "
      ],
      "metadata": {
        "id": "o3JHSOGBlBqN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def read_xquad(language, is_url=True):\n",
        "  if is_url:\n",
        "    path=base_url.format(dataset[language])\n",
        "    response = urlopen(path)\n",
        "    squad_dict = json.loads(response.read())\n",
        "  else:\n",
        "    path = Path(path)\n",
        "    squad_dict = json.load(path)\n",
        "  \n",
        "  data=[]\n",
        "  \n",
        "  for group in squad_dict['data']:\n",
        "    for passage in group['paragraphs']:\n",
        "      context = passage['context']\n",
        "      for qa in passage['qas']:\n",
        "        question = qa['question']\n",
        "        for answer in qa['answers']:\n",
        "          data.append((context, question, answer))\n",
        "  return data\n",
        "\n",
        "data_XQUAD={}\n",
        "for language in dataset.keys(): \n",
        "  data_XQUAD[language]={}\n",
        "  language_XQUAD_data = read_xquad(language)\n",
        "  _, test_data = split_squad(language_XQUAD_data, train_size=10)\n",
        "  data_XQUAD[language]['test_contexts'] = test_data[0]\n",
        "  data_XQUAD[language]['test_questions'] = test_data[1]\n",
        "  data_XQUAD[language]['test_answers'] = test_data[2]"
      ],
      "metadata": {
        "id": "0ebvAUo-mAqS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### BERT-XQuAD "
      ],
      "metadata": {
        "id": "-lbWhFgXpRGN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name='BERT-XQuAD'\n",
        "url_model=models[model_name]\n",
        "qa_pipeline = pipeline(\"question-answering\", model=url_model, tokenizer=url_model)\n",
        "\n",
        "print(model_name)\n",
        "print('_'*70+'\\n'+'Results over Cross-XQuAD', end='\\n'+'_'*70)\n",
        "evaluate_XQuAD(qa_pipeline, data_XQUAD)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "IPvYP_wXpdoB",
        "outputId": "37319ead-2c98-4be4-d69b-c96be102dfea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BERT-XQuAD\n",
            "______________________________________________________________________\n",
            "Results over Cross-XQuAD\n",
            "______________________________________________________________________"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            exact_match          f1\n",
              "language                           \n",
              "English      100.000000  100.000000\n",
              "Spanish       96.078431   98.996265\n",
              "German        98.039216   98.529412\n",
              "Vietnamese    94.117647   96.843837\n",
              "Russian       92.156863   95.866791\n",
              "Turkish       92.156863   95.447962\n",
              "Chinese       94.117647   94.117647\n",
              "Hindi         86.274510   93.672644\n",
              "Arabic        88.235294   92.893055\n",
              "Greek         78.431373   89.876284\n",
              "Romanian      72.549020   89.409360\n",
              "Thai          70.588235   80.485528"
            ],
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              "      <th></th>\n",
              "      <th>exact_match</th>\n",
              "      <th>f1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>language</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>English</th>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Spanish</th>\n",
              "      <td>96.078431</td>\n",
              "      <td>98.996265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>German</th>\n",
              "      <td>98.039216</td>\n",
              "      <td>98.529412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vietnamese</th>\n",
              "      <td>94.117647</td>\n",
              "      <td>96.843837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Russian</th>\n",
              "      <td>92.156863</td>\n",
              "      <td>95.866791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Turkish</th>\n",
              "      <td>92.156863</td>\n",
              "      <td>95.447962</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chinese</th>\n",
              "      <td>94.117647</td>\n",
              "      <td>94.117647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hindi</th>\n",
              "      <td>86.274510</td>\n",
              "      <td>93.672644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arabic</th>\n",
              "      <td>88.235294</td>\n",
              "      <td>92.893055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Greek</th>\n",
              "      <td>78.431373</td>\n",
              "      <td>89.876284</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Romanian</th>\n",
              "      <td>72.549020</td>\n",
              "      <td>89.409360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thai</th>\n",
              "      <td>70.588235</td>\n",
              "      <td>80.485528</td>\n",
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              "\n",
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              "\n",
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              "    }\n",
              "  </style>\n",
              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4efc6155-61d7-485a-bbba-a8bceed41dad');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### BERT-CrossXQuAD usando solo un ejemplo de entrenamiento por idioma"
      ],
      "metadata": {
        "id": "SgpdCpbqqQOG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name='BERT-CrossXQuAD-1Sample'\n",
        "url_model=models[model_name]\n",
        "qa_pipeline = pipeline(\"question-answering\", model=url_model, tokenizer=url_model)\n",
        "\n",
        "print(model_name)\n",
        "print('_'*70+'\\n'+'Results over Cross-XQuAD', end='\\n'+'_'*70)\n",
        "evaluate_XQuAD(qa_pipeline, data_XQUAD)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "nr-tEiriqVy-",
        "outputId": "42203ae4-1404-48a9-dedb-bc055e464c1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BERT-CrossXQuAD-1Sample\n",
            "______________________________________________________________________\n",
            "Results over Cross-XQuAD\n",
            "______________________________________________________________________"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            exact_match         f1\n",
              "language                          \n",
              "English       90.196078  90.196078\n",
              "Spanish       84.313725  87.058824\n",
              "Russian       80.392157  84.973686\n",
              "Chinese       78.431373  81.045752\n",
              "German        78.431373  80.429864\n",
              "Turkish       74.509804  79.282877\n",
              "Vietnamese    74.509804  79.221802\n",
              "Arabic        66.666667  74.064019\n",
              "Greek         64.705882  69.680083\n",
              "Hindi         62.745098  68.835739\n",
              "Romanian      49.019608  61.796395\n",
              "Thai          39.215686  43.267974"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>exact_match</th>\n",
              "      <th>f1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>language</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>English</th>\n",
              "      <td>90.196078</td>\n",
              "      <td>90.196078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Spanish</th>\n",
              "      <td>84.313725</td>\n",
              "      <td>87.058824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Russian</th>\n",
              "      <td>80.392157</td>\n",
              "      <td>84.973686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chinese</th>\n",
              "      <td>78.431373</td>\n",
              "      <td>81.045752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>German</th>\n",
              "      <td>78.431373</td>\n",
              "      <td>80.429864</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Turkish</th>\n",
              "      <td>74.509804</td>\n",
              "      <td>79.282877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vietnamese</th>\n",
              "      <td>74.509804</td>\n",
              "      <td>79.221802</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arabic</th>\n",
              "      <td>66.666667</td>\n",
              "      <td>74.064019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Greek</th>\n",
              "      <td>64.705882</td>\n",
              "      <td>69.680083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hindi</th>\n",
              "      <td>62.745098</td>\n",
              "      <td>68.835739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Romanian</th>\n",
              "      <td>49.019608</td>\n",
              "      <td>61.796395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thai</th>\n",
              "      <td>39.215686</td>\n",
              "      <td>43.267974</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-341c2578-ffdb-426e-9a98-f3e71948b3de button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-341c2578-ffdb-426e-9a98-f3e71948b3de');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### BERT-CrossXQuAD usando 2 ejemplos de entrenamiento por idioma"
      ],
      "metadata": {
        "id": "ZXpJxqnTqXIf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name='BERT-CrossXQuAD-2Samples'\n",
        "url_model=models[model_name]\n",
        "qa_pipeline = pipeline(\"question-answering\", model=url_model, tokenizer=url_model)\n",
        "\n",
        "print(model_name)\n",
        "print('_'*70+'\\n'+'Results over Cross-XQuAD', end='\\n'+'_'*70)\n",
        "evaluate_XQuAD(qa_pipeline, data_XQUAD)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "DIFXBlSYqaGf",
        "outputId": "b7b9b52e-796e-400a-9e6c-2decf18c204f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BERT-CrossXQuAD-2Samples\n",
            "______________________________________________________________________\n",
            "Results over Cross-XQuAD\n",
            "______________________________________________________________________"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            exact_match         f1\n",
              "language                          \n",
              "Spanish       66.666667  75.355933\n",
              "English       62.745098  70.504202\n",
              "Vietnamese    58.823529  62.732710\n",
              "Russian       56.862745  61.089466\n",
              "Turkish       50.980392  59.326450\n",
              "Hindi         54.901961  59.311930\n",
              "Arabic        52.941176  57.833082\n",
              "German        50.980392  56.699346\n",
              "Greek         50.980392  53.071895\n",
              "Chinese       45.098039  52.941176\n",
              "Romanian      27.450980  42.312364\n",
              "Thai          31.372549  35.620915"
            ],
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>exact_match</th>\n",
              "      <th>f1</th>\n",
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              "    <tr>\n",
              "      <th>language</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Spanish</th>\n",
              "      <td>66.666667</td>\n",
              "      <td>75.355933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>English</th>\n",
              "      <td>62.745098</td>\n",
              "      <td>70.504202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vietnamese</th>\n",
              "      <td>58.823529</td>\n",
              "      <td>62.732710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Russian</th>\n",
              "      <td>56.862745</td>\n",
              "      <td>61.089466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Turkish</th>\n",
              "      <td>50.980392</td>\n",
              "      <td>59.326450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hindi</th>\n",
              "      <td>54.901961</td>\n",
              "      <td>59.311930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arabic</th>\n",
              "      <td>52.941176</td>\n",
              "      <td>57.833082</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>German</th>\n",
              "      <td>50.980392</td>\n",
              "      <td>56.699346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Greek</th>\n",
              "      <td>50.980392</td>\n",
              "      <td>53.071895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chinese</th>\n",
              "      <td>45.098039</td>\n",
              "      <td>52.941176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Romanian</th>\n",
              "      <td>27.450980</td>\n",
              "      <td>42.312364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thai</th>\n",
              "      <td>31.372549</td>\n",
              "      <td>35.620915</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c5946339-de4c-45ed-ba88-ebd5006733be')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "    }\n",
              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-c5946339-de4c-45ed-ba88-ebd5006733be button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c5946339-de4c-45ed-ba88-ebd5006733be');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### BERT-CrossXQuAD usando 5 ejemplos de entrenamiento por idioma"
      ],
      "metadata": {
        "id": "wvEb_Q8mqeOl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name='BERT-CrossXQuAD-5Samples'\n",
        "url_model=models[model_name]\n",
        "qa_pipeline = pipeline(\"question-answering\", model=url_model, tokenizer=url_model)\n",
        "\n",
        "print(model_name)\n",
        "print('_'*70+'\\n'+'Results over Cross-XQuAD', end='\\n'+'_'*70)\n",
        "evaluate_XQuAD(qa_pipeline, data_XQUAD)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "982o3KR3qhOF",
        "outputId": "d4b81845-46f7-413d-b6dd-f931ff013f2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BERT-CrossXQuAD-5Samples\n",
            "______________________________________________________________________\n",
            "Results over Cross-XQuAD\n",
            "______________________________________________________________________"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            exact_match         f1\n",
              "language                          \n",
              "Spanish       78.431373  80.694197\n",
              "English       72.549020  75.014006\n",
              "German        70.588235  71.926548\n",
              "Vietnamese    66.666667  71.401598\n",
              "Russian       64.705882  68.302351\n",
              "Turkish       58.823529  63.875865\n",
              "Greek         56.862745  62.602496\n",
              "Arabic        58.823529  61.241830\n",
              "Hindi         54.901961  57.415787\n",
              "Chinese       49.019608  51.633987\n",
              "Romanian      33.333333  43.872703\n",
              "Thai          31.372549  35.620915"
            ],
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              "\n",
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              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>exact_match</th>\n",
              "      <th>f1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>language</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Spanish</th>\n",
              "      <td>78.431373</td>\n",
              "      <td>80.694197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>English</th>\n",
              "      <td>72.549020</td>\n",
              "      <td>75.014006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>German</th>\n",
              "      <td>70.588235</td>\n",
              "      <td>71.926548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vietnamese</th>\n",
              "      <td>66.666667</td>\n",
              "      <td>71.401598</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Russian</th>\n",
              "      <td>64.705882</td>\n",
              "      <td>68.302351</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Turkish</th>\n",
              "      <td>58.823529</td>\n",
              "      <td>63.875865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Greek</th>\n",
              "      <td>56.862745</td>\n",
              "      <td>62.602496</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arabic</th>\n",
              "      <td>58.823529</td>\n",
              "      <td>61.241830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hindi</th>\n",
              "      <td>54.901961</td>\n",
              "      <td>57.415787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chinese</th>\n",
              "      <td>49.019608</td>\n",
              "      <td>51.633987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Romanian</th>\n",
              "      <td>33.333333</td>\n",
              "      <td>43.872703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thai</th>\n",
              "      <td>31.372549</td>\n",
              "      <td>35.620915</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-63eb3de6-ae61-440b-85c5-e2cf71a91809 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-63eb3de6-ae61-440b-85c5-e2cf71a91809');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### BERT-CrossXQuAD usando 10 ejemplos de entrenamiento por idioma"
      ],
      "metadata": {
        "id": "6lsZ-cuhqnce"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name='BERT-CrossXQuAD-10Samples'\n",
        "url_model=models[model_name]\n",
        "qa_pipeline = pipeline(\"question-answering\", model=url_model, tokenizer=url_model)\n",
        "\n",
        "print(model_name)\n",
        "print('_'*70+'\\n'+'Results over Cross-XQuAD', end='\\n'+'_'*70)\n",
        "evaluate_XQuAD(qa_pipeline, data_XQUAD)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "Pr-pGqNqqqjA",
        "outputId": "93b4baa2-235d-4332-d5a4-da5e3723ef42"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BERT-CrossXQuAD-10Samples\n",
            "______________________________________________________________________\n",
            "Results over Cross-XQuAD\n",
            "______________________________________________________________________"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            exact_match         f1\n",
              "language                          \n",
              "Spanish       58.823529  65.423647\n",
              "English       58.823529  62.088151\n",
              "Turkish       47.058824  51.548672\n",
              "Vietnamese    43.137255  49.505026\n",
              "German        45.098039  48.311547\n",
              "Hindi         41.176471  45.586440\n",
              "Chinese       39.215686  44.444444\n",
              "Arabic        39.215686  42.110177\n",
              "Greek         37.254902  40.616246\n",
              "Russian       33.333333  34.580610\n",
              "Romanian      23.529412  29.764193\n",
              "Thai          21.568627  25.947712"
            ],
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              "      <th>exact_match</th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Spanish</th>\n",
              "      <td>58.823529</td>\n",
              "      <td>65.423647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>English</th>\n",
              "      <td>58.823529</td>\n",
              "      <td>62.088151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Turkish</th>\n",
              "      <td>47.058824</td>\n",
              "      <td>51.548672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vietnamese</th>\n",
              "      <td>43.137255</td>\n",
              "      <td>49.505026</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>German</th>\n",
              "      <td>45.098039</td>\n",
              "      <td>48.311547</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hindi</th>\n",
              "      <td>41.176471</td>\n",
              "      <td>45.586440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chinese</th>\n",
              "      <td>39.215686</td>\n",
              "      <td>44.444444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arabic</th>\n",
              "      <td>39.215686</td>\n",
              "      <td>42.110177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Greek</th>\n",
              "      <td>37.254902</td>\n",
              "      <td>40.616246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Russian</th>\n",
              "      <td>33.333333</td>\n",
              "      <td>34.580610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Romanian</th>\n",
              "      <td>23.529412</td>\n",
              "      <td>29.764193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thai</th>\n",
              "      <td>21.568627</td>\n",
              "      <td>25.947712</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "          document.querySelector('#df-163fd09c-18e5-46b9-a794-f4faea21e0a9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-163fd09c-18e5-46b9-a794-f4faea21e0a9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### BERT-CrossXQuAD usando 20 ejemplos de entrenamiento por idioma"
      ],
      "metadata": {
        "id": "BvZBNMwIqyFb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name='BERT-CrossXQuAD-20Samples'\n",
        "url_model=models[model_name]\n",
        "qa_pipeline = pipeline(\"question-answering\", model=url_model, tokenizer=url_model)\n",
        "\n",
        "print(model_name)\n",
        "print('_'*70+'\\n'+'Results over Cross-XQuAD', end='\\n'+'_'*70)\n",
        "evaluate_XQuAD(qa_pipeline, data_XQUAD)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "qljOjg0Xq1Pr",
        "outputId": "9148bf14-a546-43bd-c0a9-dddb73365f34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BERT-CrossXQuAD-20Samples\n",
            "______________________________________________________________________\n",
            "Results over Cross-XQuAD\n",
            "______________________________________________________________________"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            exact_match         f1\n",
              "language                          \n",
              "Spanish       74.509804  79.315565\n",
              "English       72.549020  79.139225\n",
              "Vietnamese    64.705882  71.594686\n",
              "German        68.627451  70.424837\n",
              "Chinese       56.862745  65.686275\n",
              "Russian       60.784314  62.284611\n",
              "Hindi         58.823529  61.710838\n",
              "Turkish       54.901961  58.169935\n",
              "Arabic        50.980392  55.402060\n",
              "Greek         43.137255  51.262716\n",
              "Romanian      31.372549  39.645394\n",
              "Thai          25.490196  28.758170"
            ],
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              "  <thead>\n",
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              "      <th>exact_match</th>\n",
              "      <th>f1</th>\n",
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              "    <tr>\n",
              "      <th>language</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Spanish</th>\n",
              "      <td>74.509804</td>\n",
              "      <td>79.315565</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>English</th>\n",
              "      <td>72.549020</td>\n",
              "      <td>79.139225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vietnamese</th>\n",
              "      <td>64.705882</td>\n",
              "      <td>71.594686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>German</th>\n",
              "      <td>68.627451</td>\n",
              "      <td>70.424837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chinese</th>\n",
              "      <td>56.862745</td>\n",
              "      <td>65.686275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Russian</th>\n",
              "      <td>60.784314</td>\n",
              "      <td>62.284611</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hindi</th>\n",
              "      <td>58.823529</td>\n",
              "      <td>61.710838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Turkish</th>\n",
              "      <td>54.901961</td>\n",
              "      <td>58.169935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arabic</th>\n",
              "      <td>50.980392</td>\n",
              "      <td>55.402060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Greek</th>\n",
              "      <td>43.137255</td>\n",
              "      <td>51.262716</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Romanian</th>\n",
              "      <td>31.372549</td>\n",
              "      <td>39.645394</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thai</th>\n",
              "      <td>25.490196</td>\n",
              "      <td>28.758170</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "    .colab-df-convert {\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    }\n",
              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-66c8e0e9-0b06-4fda-af42-b525d9187ece button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-66c8e0e9-0b06-4fda-af42-b525d9187ece');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### BERT-CrossXQuAD usando 25 ejemplos de entrenamiento por idioma"
      ],
      "metadata": {
        "id": "EYznFwv9q4bq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name='BERT-CrossXQuAD-25Samples'\n",
        "url_model=models[model_name]\n",
        "qa_pipeline = pipeline(\"question-answering\", model=url_model, tokenizer=url_model)\n",
        "\n",
        "print(model_name)\n",
        "print('_'*70+'\\n'+'Results over Cross-XQuAD', end='\\n'+'_'*70)\n",
        "evaluate_XQuAD(qa_pipeline, data_XQUAD)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "iRZydCo1q5L1",
        "outputId": "cca8504f-545e-4897-df2b-6eea40476824"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BERT-CrossXQuAD-25Samples\n",
            "______________________________________________________________________\n",
            "Results over Cross-XQuAD\n",
            "______________________________________________________________________"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            exact_match         f1\n",
              "language                          \n",
              "English       70.588235  74.198568\n",
              "Spanish       64.705882  68.951277\n",
              "Vietnamese    60.784314  64.851126\n",
              "Turkish       56.862745  64.604514\n",
              "Russian       58.823529  63.883584\n",
              "German        58.823529  61.546841\n",
              "Hindi         54.901961  58.300654\n",
              "Arabic        50.980392  56.239839\n",
              "Chinese       50.980392  55.882353\n",
              "Greek         49.019608  55.732960\n",
              "Romanian      29.411765  41.411195\n",
              "Thai          23.529412  27.124183"
            ],
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              "      <th>exact_match</th>\n",
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              "    <tr>\n",
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              "      <td>70.588235</td>\n",
              "      <td>74.198568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Spanish</th>\n",
              "      <td>64.705882</td>\n",
              "      <td>68.951277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vietnamese</th>\n",
              "      <td>60.784314</td>\n",
              "      <td>64.851126</td>\n",
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              "      <td>56.862745</td>\n",
              "      <td>64.604514</td>\n",
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              "      <td>63.883584</td>\n",
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              "      <td>61.546841</td>\n",
              "    </tr>\n",
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              "    </tr>\n",
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              "      <th>Arabic</th>\n",
              "      <td>50.980392</td>\n",
              "      <td>56.239839</td>\n",
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              "      <td>50.980392</td>\n",
              "      <td>55.882353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Greek</th>\n",
              "      <td>49.019608</td>\n",
              "      <td>55.732960</td>\n",
              "    </tr>\n",
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              "      <th>Romanian</th>\n",
              "      <td>29.411765</td>\n",
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